Financial Econometrics Using Stata

Financial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.

Introduction to financial time series The object of interest Approaching the dataset Normality Stationarity Autocorrelation Heteroskedasticity Linear time series Model selection How to import data ARMA models Autoregressive (AR) processes Moving-average (MA) processes Autoregressive moving-average (ARMA) processes Application of ARMA models Modeling volatilities, ARCH models, and GARCH models Introduction ARCH models ARCH(p) GARCH models Asymmetric GARCH models Alternative GARCH models Multivariate GARCH models Introduction Multivariate GARCH Direct generalizations of the univariate GARCH model of Bollerslev Nonlinear combination of univariate GARCH—common features Final remarks Risk management Introduction Loss Risk measures VaR Backtesting procedures Contagion analysis Introduction Contagion measurement

Simona Boffelli, PhD, is a quantitative analyst at Fineco Bank in Milan, part of the Unicredit Group. She is a researcher associate to the Department of Management, Economics and Quantitative Methods of Bergamo University in Italy and to the Centre for Econometric Analysis of Cass Business School in London. Giovanni Urga, PhD, is a professor of finance and econometrics and the director of the Centre for Econometric Analysis at Cass Business School in London, and is a professor of econometrics at the Department of Management, Economics and Quantitative Methods of Bergamo University in Italy.